


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					(UnCLIPScheduler,)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   **_UpperCAmelCase       :		Union[str, Any]						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						{
							    """num_train_timesteps""": 1000,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**_UpperCAmelCase						)
							return config

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  str:




							"""simple docstring"""


							for timesteps in [1, 5, 100, 1000]:
									self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Tuple						)		->  Union[str, Any]:




							"""simple docstring"""


							for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Tuple						)		->  List[Any]:




							"""simple docstring"""


							for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  List[Any]:




							"""simple docstring"""


							for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  Union[str, Any]:




							"""simple docstring"""


							for time_step in [0, 500, 999]:
									for prev_timestep in [None, 5, 100, 250, 500, 750]:
											if prev_timestep is not None and prev_timestep >= time_step:
													continue

											self.check_over_forward(time_step=_UpperCAmelCase ,   prev_timestep=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config(variance_type="""fixed_small_log"""						)
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0_0_0_0E-1_0						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.0_549_625						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.9_994_987						)						) < 1E-5

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config(variance_type="""learned_range"""						)
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							lowercase__	     =						0.5

							assert scheduler._get_variance(1 ,   predicted_variance=_UpperCAmelCase						) - -10.1_712_790 < 1E-5
							assert scheduler._get_variance(487 ,   predicted_variance=_UpperCAmelCase						) - -5.7_998_052 < 1E-5
							assert scheduler._get_variance(999 ,   predicted_variance=_UpperCAmelCase						) - -0.0_010_011 < 1E-5

					def     lowerCamelCase__	(self       :		str						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config()
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							lowercase__	     =						scheduler.timesteps

							lowercase__	     =						self.dummy_model()
							lowercase__	     =						self.dummy_sample_deter
							lowercase__	     =						torch.manual_seed(0						)

							for i, t in enumerate(_UpperCAmelCase						):
									# 1. predict noise residual
									lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						)

									# 2. predict previous mean of sample x_t-1
									lowercase__	     =						scheduler.step(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   generator=_UpperCAmelCase						).prev_sample

									lowercase__	     =						pred_prev_sample

							lowercase__	     =						torch.sum(torch.abs(_UpperCAmelCase						)						)
							lowercase__	     =						torch.mean(torch.abs(_UpperCAmelCase						)						)

							assert abs(result_sum.item() - 252.2_682_495						) < 1E-2
							assert abs(result_mean.item() - 0.3_284_743						) < 1E-3

					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config()
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							scheduler.set_timesteps(25						)

							lowercase__	     =						scheduler.timesteps

							lowercase__	     =						self.dummy_model()
							lowercase__	     =						self.dummy_sample_deter
							lowercase__	     =						torch.manual_seed(0						)

							for i, t in enumerate(_UpperCAmelCase						):
									# 1. predict noise residual
									lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						)

									if i + 1 == timesteps.shape[0]:
											lowercase__	     =						None
									else:
											lowercase__	     =						timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									lowercase__	     =						scheduler.step(
									    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   prev_timestep=_UpperCAmelCase ,   generator=_UpperCAmelCase						).prev_sample

									lowercase__	     =						pred_prev_sample

							lowercase__	     =						torch.sum(torch.abs(_UpperCAmelCase						)						)
							lowercase__	     =						torch.mean(torch.abs(_UpperCAmelCase						)						)

							assert abs(result_sum.item() - 258.2_044_983						) < 1E-2
							assert abs(result_mean.item() - 0.3_362_038						) < 1E-3

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  int:




							"""simple docstring"""


							pass


					def     lowerCamelCase__	(self       :		List[str]						)		->  str:




							"""simple docstring"""


							pass





'''simple docstring'''


import numpy as np





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= (0, 0)
											_SCREAMING_SNAKE_CASE					:			List[str]							= None
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0
											_SCREAMING_SNAKE_CASE					:			List[str]							= 0
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0






							def __eq__(       self       ,   __snake_case    ):
											return self.position == cell.position






							def UpperCAmelCase_      (       self    ):
											print(self.position    )





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case=(5, 5)    ):
											_SCREAMING_SNAKE_CASE					:			str							= np.zeros(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= world_size[0]
											_SCREAMING_SNAKE_CASE					:			Tuple							= world_size[1]






							def UpperCAmelCase_      (       self    ):
											print(self.w    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= [
											    (-1, -1),
											    (-1, 0),
											    (-1, 1),
											    (0, -1),
											    (0, 1),
											    (1, -1),
											    (1, 0),
											    (1, 1),
											]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[0]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[1]
											_SCREAMING_SNAKE_CASE					:			Tuple							= []
											for n in neughbour_cord:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= current_x + n[0]
															_SCREAMING_SNAKE_CASE					:			List[Any]							= current_y + n[1]
															if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= Cell()
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (x, y)
																			_SCREAMING_SNAKE_CASE					:			Dict							= cell
																			neighbours.append(__snake_case    )
											return neighbours







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= []
				_open.append(SCREAMING_SNAKE_CASE__	)

				while _open:
								_SCREAMING_SNAKE_CASE					:			Any							= np.argmin([n.f for n in _open]	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= _open[min_f]
								_closed.append(_open.pop(SCREAMING_SNAKE_CASE__	)	)
								if current == goal:
												break
								for n in world.get_neigbours(SCREAMING_SNAKE_CASE__	):
												for c in _closed:
																if c == n:
																				continue
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= current.g + 1
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= n.position
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= goal.position
												_SCREAMING_SNAKE_CASE					:			Dict							= (ya - ya) ** 2 + (xa - xa) ** 2
												_SCREAMING_SNAKE_CASE					:			Any							= n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= []
				while current.parent is not None:
								path.append(current.position	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= current.parent
				path.append(current.position	)
				return path[::-1]


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						Gridworld()
				# Start position and goal
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(0, 0)
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(4, 4)
				print(F"path from {start.position} to {goal.position}")
				UpperCAmelCase_						:  Union[str, Any]				  =						astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								UpperCAmelCase_						:  Optional[int]				  =						1
				print(world.w)




